Developing models for the prediction of hospital healthcare waste generation rate

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1 607422WMR / X Waste Management & ResearchTesfahun et al. research-article2015 Original Article Developing models for the prediction of hospital healthcare waste generation rate Waste Management & Research 2016, Vol. 34(1) The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalspermissions.nav DOI: / X wmr.sagepub.com Esubalew Tesfahun 1, Abera Kumie 1 and Abebe Beyene 2 Abstract An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R 2 = 0.965), and a weak one with the number of outpatients (R 2 = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. Keywords Healthcare waste, generation rate, hospitals, prediction, models, Ethiopia Introduction All over the world, there is a continued growth in the number of hospitals and other health facilities in relation to meet the healthcare demand of the alarming population growth. An increase in number of health institutions, combined with an increase in the use of disposable medical products, has contributed to the large amount of healthcare waste being generated (Karamouz et al., 2007; World Health Organization, 2002). The high generation rate compounded by poor handling and disposal practices (Hassan et al., 2008) has increased the risk of environmental pollution and diseases transmission (Awad et al., 2004). Owing to these facts, installation of an integrated healthcare waste management system for health institutions is becoming a cross-cutting issue (Nema et al., 2011). Many researchers have argued that the availability of enough information about the amount and composition of the healthcare waste generated is the first step for the implementation of sound waste management systems (Altin et al., 2003; Karamouz et al., 2007; Taghipour and Mosaferi, 2009). In addition, healthcare waste should be characterised by source, generation rates, types of waste produced and composition in order to monitor and control the existing healthcare waste management systems (Altin et al., 2003). Therefore, development of models is not only a necessity, but millstones for proper healthcare waste management system. The generation rate of healthcare waste varies among the type of hospitals (government, private and teaching hospitals) (Rahele and Govindan, 2013). Hence, the development of predictive models should consider the type of hospitals. The amount of different kinds of healthcare waste generated at hospitals can be determined by identifying the relationship between the weight of the healthcare waste generated and main important factors that affect healthcare waste generation rate, such as type of hospital, hospital specialisation, hospital size and proportion of patients treated on a daily basis (Askarian et al., 2004; Cheng et al., 2009; Razali and Ishak, 2010; Silva et al., 2005; Tudor et al., 2005). Statistical analysis is one option to evaluate the relationship between these important factors and the amount of healthcare waste generated (Eker and Bilgili, 2011) in each type of hospital that most predictive models lack. The study conducted by Bdour et al. (2007) in Jordan, confirmed that high statistically significant (linear) correlation exists between the number of patients (R 2 = 0.945) and the number of beds (R 2 = 905) with the amount of daily healthcare waste 1 School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia 2 Department of Environmental Health Science & Technology, Jimma University, Jimma, Ethiopia Corresponding author: Esubalew Tesfahun, School of Public Health, College of Health Science, Addis Ababa University, PO Box , Addis Ababa, Ethiopia. esubalew.tesfahun@gmail.com

2 76 Waste Management & Research 34(1) generated (Bdour et al., 2007). The study done in Irbid, Jordan, by Awad et al. (2004), indicated that the quantity of the healthcare waste generation rate has a strong correlation with the number of patients, number of beds and type of hospital at R 2 = 0.973, R 2 = and R 2 = 0.368, respectively (Awad et al., 2004). Similarly, the study conducted by Komilis et al. (2011) indicated a linear statistically significant correlation at p < 0.05 with R 2 = 0.43 between the daily healthcare waste generation rate and the number of beds occupied (Komilis et al., 2011). The study done by Katoch and Kumar (2007) in India confirmed that the seasonal variation in the biomedical waste production rate remained nearly the same (Katoch and Kumar, 2007). Determining an advance healthcare waste generation rate using a mathematical predictive model can help to optimise healthcare waste management systems to set guidelines and to evaluate the prevailing strategies for healthcare waste handling, as well as proper disposal (Katoch and Kumar, 2007). From the review of available literatures, we confirmed that only a few predictive models are available. The available models are presented in equations (1) to (4): Generation rate in kg day = (PAT) (BED) (Type) where PAT is the number of patients, BED is the number of beds and TYPE is the type of hospital (Awad et al., 2004). Monthly average biomedical waste generation rate (Wo) of hospitals in kg day in terms of average bed occupancy rate, B (beds day ) equals: (1) 2 Wo = K 1+ K 2. B + K 3 *B (2) where, coefficients K 1, K 2 and K 3 are constants for a particular type of hospital (Katoch and Kumar, 2007). Generation rate in kg day = (PAT) (CAP) where PAT is the number of inpatients and CAP is the number of beds (Idowu et al., 2013). (3) Y = (T hb *W hb) + (T cb * W cb) + (T dt * W dt ) (4) where Y is the total healthcare waste generated per day in kg day, T hb is the total number of hospital beds in sampled facilities, W hb is the average waste per hospital bed per day in sampled hospitals; T cb is the total number of clinic beds in sampled facilities, W cb is the average waste per clinic bed per day in sampled clinics, T dt is the total number of diagnostic centres tests per day in sampled facilities and W dt is the average waste per diagnostic test in sampled diagnostic centres (Patwary et al., 2009). All the equations (1) to (4) shows that the healthcare waste generation rate predictive models varied based on the difference of the study area. This indicates that estimating the generation rate of healthcare wastes in developing countries should consider the local determinant factors. Nevertheless, there is no predictive model available in Ethiopia that can be used for predicting a healthcare waste generation rate. Therefore, this research has a paramount importance for improving a healthcare waste management system at a local and national level. Methods and materials Study area The Amhara National Regional State has a total of 18 public hospitals (two teaching, three referrals, two zonals and 11 districts) and six general private hospitals, all of them located in 11 different towns. From these hospitals, we proportionally and randomly selected six governmental hospitals (one teaching, one referral, one zonal and three district hospitals) and three general private hospitals. The towns vary in size from small (Boru media) to medium (Debre Birhan) and large (Gondar), which correspond to the size and functionality of the study hospitals. The total number of beds per hospital varied between private and public hospitals and among public hospitals: Ayu private hospital (64 beds), Selam private hospital (62), IBEX private hospital (32), Boru district hospital (80), Enat district hospital (54), Mehal Meda district hospital (41), Debre Tabor zonal hospital (89), Debre Birhan referral hospital (135) and Gondar teaching hospital (512). Study design A longitudinal study design was conducted to generate long-term solid healthcare waste composition and generation rate. The long-term data were used to develop predictive models that can precisely estimate the generation rates of hospital healthcare wastes. This study design allows capturing variations and composition of healthcare waste generation. Data collection Out of the total 24 hospitals, the data were collected from nine proportionally allocated hospitals with different levels of specialisation, capacity and ownership (private and government) and randomly selected hospitals. The data collection was conducted in two rounds. The first round was conducted from November to December 2013 (dry season) and in the second round from June to July 2014 (wet season). The data collectors had secondary school certificates, and supervisors had BSc degrees in Environmental Health. In both the first and second round of data collection, one days training was given about data collection, demonstration of data collection tools and protocols for data collectors and supervisors. All waste collection buckets (black, yellow, red and blue colour for general, infectious, pathological and pharmaceutical healthcare waste, respectively), safety boxes and plastic bags were labelled to indicate the different categories of healthcare waste, date of collection and sample number. The quantity of waste generated was estimated by collecting and weighing healthcare waste from all departments of the study hospitals using a calibrated sensitive weight scale CTG 31 model

3 Tesfahun et al. 77 made in India every day at 12:00 pm for seven consecutive days (Monday Sunday) during both rounds. The waste characterisation was done in accordance with World Health Organization (WHO) guidelines (Prüss et al., 2013). The daily generation of waste, together with the number of beds occupied and patients treated in outpatient departments, were recorded daily. As described by different authors, the healthcare waste generation rates were estimated on the basis of kg bed day and kg outpatient day (Awad et al., 2004; Dagnew et al., 2009). Statistical analysis We used EPI-INFO 7 for data entry and SPSS version 16 for data analysis. The analysis was performed separately for each of the nine hospitals, grouped by public and private hospitals, and by category of healthcare waste. First, we explored the distribution of the healthcare waste generation data, including normality using a normality test, which showed that the data were normally distributed. Therefore, we used Pearson correlation test for the bivariate associations. In this study, the important variables that affect the quantity of wastes generated from the hospitals were identified, then multivariate linear regression analysis was applied in order to develop predictable models that can be used in estimating or predicting the waste generation rate in sampled hospitals. Establishing the simple correlation matrices between different variables was the first step in model development. This step was helpful to investigate the strength and form of the relationship between the variables included in the analysis. In order to see the effect of the parameters and their confidence levels on the waste generation rate in healthcare services, analysis of variance (ANOVA) was performed to compare the rate by the type of hospitals. The F-test was a tool to see which parameters had a significant effect on the removal efficiency. The data quality was ensured by using calibrated instruments, experienced professional supervisors, training of supervisors and data collectors and daily on-site supervision was made by the investigator during the actual measurements. Results and discussion The two-round data collected from the sampled hospitals showed that the mean generation rates in kg bed day were 1.14, 0.74, 0.21, 0.27, 0.09 and 0.02 for general, infectious, sharps, pharmaceutical, pathological and radioactive healthcare waste, respectively. The percentage compositions were 46.32%, 33.95%, 4.04%, 9.67%, 5.78% and 0.24% for general, infectious, sharps, pharmaceutical, pathological and radioactive healthcare waste, respectively. These results have significant differences compared with World Health Organization (WHO) reports of 80%, 15%, 1%, 3% and less than 1% for general, pathological and infectious, sharps, pharmaceutical and radioactive healthcare waste (Prüss et al., 2013; World Health Organization, 2005). The reason might be the absence of segregation practices in the sample hospitals. In this study, the important variables (number of inpatients and outpatients) that mainly affect the healthcare waste generation rate were identified using correlation. Linear regression analysis was done in order to develop predictable models. It was observed that the healthcare waste generation rate has a strong correlation with the number of inpatients (R 2 = 0.965, P < ), the number of outpatients (R 2 = 0.424, P < ) and number of total patients (R 2 = 0.802, P < ). The results showed a stronger positive correlation of the healthcare waste generation rate with the number of inpatients than with the number This is owing to longer hospital stays of inpatients with services in the hospitals. Such positive correlations are also reported by Komilis et al. (2011) in Greece and Idowu et al. (2013) in Nigeria. The bed occupancy rates were 78.3%, 69.6%, 55.5%, 41.6%, 39.0%, 28.8%, 17.7%, 12.5% and 3.1% for Gondar teaching, Debre Birhan referral, Enat district, Debre Tabor zonal, Mehal Meda district, Boru district, Selam private, Ayu private and IBEX private hospitals, respectively. The waste generation rates in kg day were 689.5, 140.2, 62.7, 56.9, 34.3, 28.4, 25.4, 10.3 and 7.6 in row of the bed occupancy rates of the hospitals. The relation between bed occupancy rate and healthcare waste generation rate have a linear relationship (R 2 = 0.5). In order to investigate the effect of each independent variable on the dependent variable, scatter plots of healthcare waste generation versus the number of inpatients, number of outpatients and total number of patients were plotted (Figures 1, 2 and 3). As shown in Figures 1, 2 and 3, the relationship of the healthcare waste generation rate among the number of inpatients, number of outpatients and total number of patients is linear. Based on the linear relationship, the following predictive models were developed and presented as equations (5) to (8). The predictive model developed for total healthcare waste generation rate derived from inpatients and outpatients is given in equation (5) and the model results are presented in Table 1. Generation rate in kg day (Y) (NOPT) = 1.26(NIPT) + Healthcare waste generation rate predictive models by hospital type The results of this study revealed that healthcare waste generation rate significantly varies based on the types of hospitals. Public referral hospitals were found to be the highest healthcare waste generators, followed by public district and private general hospitals in their order. This finding is also in agreement with the research reports of Awad et al. (2004). Hence, to increase the accuracy of prediction, it is required to develop separate predictive models for different type of hospitals (private general, district public and referral public hospitals). The predictive models for three categories of hospitals are presented in equations (6), (7) and (8) and their results are given in Table 2. (5)

4 78 Waste Management & Research 34(1) Predictive model for private general hospitals. Generation rate in kg day (Y) = (NIPT) (NOPT) (6) Predictive model for public district hospitals. Generation rate in kg day (Y) = (NIPT) (NOPT) (7) Figure 1. Daily healthcare waste generation versus number of inpatients in all hospitals. Predictive model for public referral hospitals. Generation rate in kg day (Y) 0.148(NOPT) = (NIPT) + (8) Figure 2. Daily healthcare waste generation versus number of outpatients in all hospitals. Figure 3. Daily healthcare waste generation versus number of total patients in all hospitals. The healthcare waste generation rate also positively correlated to the number of inpatients (R 2 = 0.657), (R 2 = 0.468) and (R 2 = 0.976) for private general, public district and public referral hospitals, respectively. The generation rate is also positively correlated with the number of outpatients (R 2 = 0.817), (R 2 = 0.210) and (R 2 = 0.699) for private general, public district and public referral hospitals, respectively. All independent variables used for the prediction of the healthcare waste generation rate were found to be statistically significant. The linear regression based on the number of outpatients and healthcare waste generation rate in district hospitals only explains 21% of the number of outpatient variables. The healthcare waste generation rate predictor variables (number of inpatient and number of outpatient treated in the hospitals) that are identified and used for the development of the predictive models in this study are similar to the research findings reported elsewhere. For instance, the studies conducted in India, Jordan, Kuwait, Greece and Taiwan confirmed that the healthcare waste generation rate is affected by the number of inpatients and outpatients in the hospital (Alhumoud and Alhumoud, 2007; Bdour et al., 2007; Katoch and Kumar, 2007). This research included different type of hospitals, such as private and government hospitals, and also different levels of hospitals, which included teaching, referral, zonal, district and general, which can represent all types of hospitals found in the healthcare system of Ethiopia. Therefore, the results may serve as a stepping stone in evaluating the success and failure of pre- and post-intervention projects, and could be useful for the development of operational guidelines for the management of healthcare waste in health facilities nationwide.

5 Tesfahun et al. 79 Table 1. Statistical characteristics of the model (equation 5). Analysis of variance Model df Sum of square Mean of square F-value α-level Regression E Residual Total R 2 = 0.927, Adjusted R 2 = Regression parameter estimate Variables Parameter estimate Standard error T-value α-level Intercept Inpatient Outpatient Note: Acceptable α-level (level of significance) = 0.100; F represents general linearity test; R 2 represents coefficient of multiple determination; df represents degree of freedom; Adjusted R 2 represents adjustment of R 2 ; and T represents importance of model variables. Table 2. Model summary of three types of sampled hospitals. Hospital type R square Adjusted R square Std error of the estimate Change statistics R square change F change df Sig F change Private general Public district Public referral Conclusion From the total healthcare waste, the major components were general and infectious wastes, which account for 80.27%. Our finding proved that both measurement units (kg inpatient day, kg outpatient day, kg sum of inpatient and outpatient day ) of the healthcare waste generation rate need to be used in combination to reliably quantify the healthcare waste generation rates, as the number of inpatients and outpatients variables were found to be the main predictors of the healthcare waste generation rate. The healthcare waste generation rate were also dependent on the type and level of hospitals, therefore, to formulate a precise predictive model for the estimation healthcare waste generation rate, separate analysis is required to be in accordance with the types and level of hospitals. The models developed in this study can assist the development of a strategic plan for the implementation of an appropriate healthcare waste management system. Acknowledgements We would like to thank the Amhara Regional State Hospital staff and administrators for their collaboration and unreserved help during data collection. Declaration of conflicting interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We are grateful to Addis Ababa University for financial and logistic support. References Alhumoud JM and Alhumoud HM (2007) An analysis of trends related to hospital solid wastes management in Kuwait. International Journal 18: Altin S, Altin A, Elevli B, et al. (2003) Determination of hospital waste composition and disposal methods: A case study. Polish Journal of Environmental Studies 12: Askarian M, Vakili M and Kabir G. (2004) Results of a hospital waste survey in private hospitals in Fars province, Iran. Waste Management 24: Awad AR, Obeidat M and Al-Shareef M. (2004) Mathematical-statistical models of generated hazardous hospital solid waste. Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering 39: Bdour A, Altrabsheh B, Hadadin N, et al. (2007) Assessment of medical wastes management practice: A case study of the northern part of Jordan. Waste Management 27: Cheng YW, Sung FC, Yang Y, et al. (2009) Medical waste production at hospitals and associated factors. Waste Management 29: Dagnew E, Hameed S and Leta S. (2009) Determining the generation rate and composition of solid health care waste at gondar university hospital. Ethiopian Journal of Health & Biomedical Sciences 2: Eker HH and Bilgili MS (2011) Statistical analysis of waste generation in healthcare services: A case study. Waste Management & Research 29:

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